/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 * Dagging.java
 * Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.meta;

import weka.classifiers.Classifier;
import weka.classifiers.RandomizableSingleClassifierEnhancer;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;

import java.util.Enumeration;
import java.util.Vector;

/**
 * <!-- globalinfo-start --> This meta classifier creates a number of disjoint,
 * stratified folds out of the data and feeds each chunk of data to a copy of
 * the supplied base classifier. Predictions are made via majority vote, since
 * all the generated base classifiers are put into the Vote meta classifier. <br/>
 * Useful for base classifiers that are quadratic or worse in time behavior,
 * regarding number of instances in the training data. <br/>
 * <br/>
 * For more information, see: <br/>
 * Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth
 * international Conference on Machine Learning, San Francisco, CA, 367-375,
 * 1997.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- technical-bibtex-start --> BibTeX:
 * 
 * <pre>
 * &#64;inproceedings{Ting1997,
 *    address = {San Francisco, CA},
 *    author = {Ting, K. M. and Witten, I. H.},
 *    booktitle = {Fourteenth international Conference on Machine Learning},
 *    editor = {D. H. Fisher},
 *    pages = {367-375},
 *    publisher = {Morgan Kaufmann Publishers},
 *    title = {Stacking Bagged and Dagged Models},
 *    year = {1997}
 * }
 * </pre>
 * <p/>
 * <!-- technical-bibtex-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -F &lt;folds&gt;
 *  The number of folds for splitting the training set into
 *  smaller chunks for the base classifier.
 *  (default 10)
 * </pre>
 * 
 * <pre>
 * -verbose
 *  Whether to print some more information during building the
 *  classifier.
 *  (default is off)
 * </pre>
 * 
 * <pre>
 * -S &lt;num&gt;
 *  Random number seed.
 *  (default 1)
 * </pre>
 * 
 * <pre>
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <pre>
 * -W
 *  Full name of base classifier.
 *  (default: weka.classifiers.functions.SMO)
 * </pre>
 * 
 * <pre>
 * Options specific to classifier weka.classifiers.functions.SMO:
 * </pre>
 * 
 * <pre>
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <pre>
 * -no-checks
 *  Turns off all checks - use with caution!
 *  Turning them off assumes that data is purely numeric, doesn't
 *  contain any missing values, and has a nominal class. Turning them
 *  off also means that no header information will be stored if the
 *  machine is linear. Finally, it also assumes that no instance has
 *  a weight equal to 0.
 *  (default: checks on)
 * </pre>
 * 
 * <pre>
 * -C &lt;double&gt;
 *  The complexity constant C. (default 1)
 * </pre>
 * 
 * <pre>
 * -N
 *  Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)
 * </pre>
 * 
 * <pre>
 * -L &lt;double&gt;
 *  The tolerance parameter. (default 1.0e-3)
 * </pre>
 * 
 * <pre>
 * -P &lt;double&gt;
 *  The epsilon for round-off error. (default 1.0e-12)
 * </pre>
 * 
 * <pre>
 * -M
 *  Fit logistic models to SVM outputs.
 * </pre>
 * 
 * <pre>
 * -V &lt;double&gt;
 *  The number of folds for the internal
 *  cross-validation. (default -1, use training data)
 * </pre>
 * 
 * <pre>
 * -W &lt;double&gt;
 *  The random number seed. (default 1)
 * </pre>
 * 
 * <pre>
 * -K &lt;classname and parameters&gt;
 *  The Kernel to use.
 *  (default: weka.classifiers.functions.supportVector.PolyKernel)
 * </pre>
 * 
 * <pre>
 * Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
 * </pre>
 * 
 * <pre>
 * -D
 *  Enables debugging output (if available) to be printed.
 *  (default: off)
 * </pre>
 * 
 * <pre>
 * -no-checks
 *  Turns off all checks - use with caution!
 *  (default: checks on)
 * </pre>
 * 
 * <pre>
 * -C &lt;num&gt;
 *  The size of the cache (a prime number), 0 for full cache and 
 *  -1 to turn it off.
 *  (default: 250007)
 * </pre>
 * 
 * <pre>
 * -E &lt;num&gt;
 *  The Exponent to use.
 *  (default: 1.0)
 * </pre>
 * 
 * <pre>
 * -L
 *  Use lower-order terms.
 *  (default: no)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * Options after -- are passed to the designated classifier.
 * <p/>
 * 
 * @author Bernhard Pfahringer (bernhard at cs dot waikato dot ac dot nz)
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 5306 $
 * @see Vote
 */
public class Dagging extends RandomizableSingleClassifierEnhancer implements
		TechnicalInformationHandler {

	/** for serialization */
	static final long serialVersionUID = 4560165876570074309L;

	/** the number of folds to use to split the training data */
	protected int m_NumFolds = 10;

	/** the classifier used for voting */
	protected Vote m_Vote = null;

	/** whether to output some progress information during building */
	protected boolean m_Verbose = false;

	/**
	 * Returns a string describing classifier
	 * 
	 * @return a description suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String globalInfo() {
		return "This meta classifier creates a number of disjoint, stratified folds out "
				+ "of the data and feeds each chunk of data to a copy of the supplied "
				+ "base classifier. Predictions are made via averaging, since all the "
				+ "generated base classifiers are put into the Vote meta classifier. \n"
				+ "Useful for base classifiers that are quadratic or worse in time "
				+ "behavior, regarding number of instances in the training data. \n"
				+ "\n"
				+ "For more information, see: \n"
				+ getTechnicalInformation().toString();
	}

	/**
	 * Returns an instance of a TechnicalInformation object, containing detailed
	 * information about the technical background of this class, e.g., paper
	 * reference or book this class is based on.
	 * 
	 * @return the technical information about this class
	 */
	public TechnicalInformation getTechnicalInformation() {
		TechnicalInformation result;

		result = new TechnicalInformation(Type.INPROCEEDINGS);
		result.setValue(Field.AUTHOR, "Ting, K. M. and Witten, I. H.");
		result.setValue(Field.TITLE, "Stacking Bagged and Dagged Models");
		result.setValue(Field.BOOKTITLE,
				"Fourteenth international Conference on Machine Learning");
		result.setValue(Field.EDITOR, "D. H. Fisher");
		result.setValue(Field.YEAR, "1997");
		result.setValue(Field.PAGES, "367-375");
		result.setValue(Field.PUBLISHER, "Morgan Kaufmann Publishers");
		result.setValue(Field.ADDRESS, "San Francisco, CA");

		return result;
	}

	/**
	 * Constructor.
	 */
	public Dagging() {
		m_Classifier = new weka.classifiers.functions.SMO();
	}

	/**
	 * String describing default classifier.
	 * 
	 * @return the default classifier classname
	 */
	protected String defaultClassifierString() {
		return weka.classifiers.functions.SMO.class.getName();
	}

	/**
	 * Returns an enumeration describing the available options.
	 * 
	 * @return an enumeration of all the available options.
	 */
	public Enumeration listOptions() {
		Vector result = new Vector();

		result.addElement(new Option(
				"\tThe number of folds for splitting the training set into\n"
						+ "\tsmaller chunks for the base classifier.\n"
						+ "\t(default 10)", "F", 1, "-F <folds>"));

		result.addElement(new Option(
				"\tWhether to print some more information during building the\n"
						+ "\tclassifier.\n" + "\t(default is off)", "verbose",
				0, "-verbose"));

		Enumeration en = super.listOptions();
		while (en.hasMoreElements())
			result.addElement(en.nextElement());

		return result.elements();
	}

	/**
	 * Parses a given list of options.
	 * <p/>
	 * 
	 * <!-- options-start --> Valid options are:
	 * <p/>
	 * 
	 * <pre>
	 * -F &lt;folds&gt;
	 *  The number of folds for splitting the training set into
	 *  smaller chunks for the base classifier.
	 *  (default 10)
	 * </pre>
	 * 
	 * <pre>
	 * -verbose
	 *  Whether to print some more information during building the
	 *  classifier.
	 *  (default is off)
	 * </pre>
	 * 
	 * <pre>
	 * -S &lt;num&gt;
	 *  Random number seed.
	 *  (default 1)
	 * </pre>
	 * 
	 * <pre>
	 * -D
	 *  If set, classifier is run in debug mode and
	 *  may output additional info to the console
	 * </pre>
	 * 
	 * <pre>
	 * -W
	 *  Full name of base classifier.
	 *  (default: weka.classifiers.functions.SMO)
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to classifier weka.classifiers.functions.SMO:
	 * </pre>
	 * 
	 * <pre>
	 * -D
	 *  If set, classifier is run in debug mode and
	 *  may output additional info to the console
	 * </pre>
	 * 
	 * <pre>
	 * -no-checks
	 *  Turns off all checks - use with caution!
	 *  Turning them off assumes that data is purely numeric, doesn't
	 *  contain any missing values, and has a nominal class. Turning them
	 *  off also means that no header information will be stored if the
	 *  machine is linear. Finally, it also assumes that no instance has
	 *  a weight equal to 0.
	 *  (default: checks on)
	 * </pre>
	 * 
	 * <pre>
	 * -C &lt;double&gt;
	 *  The complexity constant C. (default 1)
	 * </pre>
	 * 
	 * <pre>
	 * -N
	 *  Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)
	 * </pre>
	 * 
	 * <pre>
	 * -L &lt;double&gt;
	 *  The tolerance parameter. (default 1.0e-3)
	 * </pre>
	 * 
	 * <pre>
	 * -P &lt;double&gt;
	 *  The epsilon for round-off error. (default 1.0e-12)
	 * </pre>
	 * 
	 * <pre>
	 * -M
	 *  Fit logistic models to SVM outputs.
	 * </pre>
	 * 
	 * <pre>
	 * -V &lt;double&gt;
	 *  The number of folds for the internal
	 *  cross-validation. (default -1, use training data)
	 * </pre>
	 * 
	 * <pre>
	 * -W &lt;double&gt;
	 *  The random number seed. (default 1)
	 * </pre>
	 * 
	 * <pre>
	 * -K &lt;classname and parameters&gt;
	 *  The Kernel to use.
	 *  (default: weka.classifiers.functions.supportVector.PolyKernel)
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
	 * </pre>
	 * 
	 * <pre>
	 * -D
	 *  Enables debugging output (if available) to be printed.
	 *  (default: off)
	 * </pre>
	 * 
	 * <pre>
	 * -no-checks
	 *  Turns off all checks - use with caution!
	 *  (default: checks on)
	 * </pre>
	 * 
	 * <pre>
	 * -C &lt;num&gt;
	 *  The size of the cache (a prime number), 0 for full cache and 
	 *  -1 to turn it off.
	 *  (default: 250007)
	 * </pre>
	 * 
	 * <pre>
	 * -E &lt;num&gt;
	 *  The Exponent to use.
	 *  (default: 1.0)
	 * </pre>
	 * 
	 * <pre>
	 * -L
	 *  Use lower-order terms.
	 *  (default: no)
	 * </pre>
	 * 
	 * <!-- options-end -->
	 * 
	 * Options after -- are passed to the designated classifier.
	 * <p>
	 * 
	 * @param options
	 *            the list of options as an array of strings
	 * @throws Exception
	 *             if an option is not supported
	 */
	public void setOptions(String[] options) throws Exception {
		String tmpStr;

		tmpStr = Utils.getOption('F', options);
		if (tmpStr.length() != 0)
			setNumFolds(Integer.parseInt(tmpStr));
		else
			setNumFolds(10);

		setVerbose(Utils.getFlag("verbose", options));

		super.setOptions(options);
	}

	/**
	 * Gets the current settings of the Classifier.
	 * 
	 * @return an array of strings suitable for passing to setOptions
	 */
	public String[] getOptions() {
		Vector result;
		String[] options;
		int i;

		result = new Vector();

		result.add("-F");
		result.add("" + getNumFolds());

		if (getVerbose())
			result.add("-verbose");

		options = super.getOptions();
		for (i = 0; i < options.length; i++)
			result.add(options[i]);

		return (String[]) result.toArray(new String[result.size()]);
	}

	/**
	 * Gets the number of folds to use for splitting the training set.
	 * 
	 * @return the number of folds
	 */
	public int getNumFolds() {
		return m_NumFolds;
	}

	/**
	 * Sets the number of folds to use for splitting the training set.
	 * 
	 * @param value
	 *            the new number of folds
	 */
	public void setNumFolds(int value) {
		if (value > 0)
			m_NumFolds = value;
		else
			System.out.println("At least 1 fold is necessary (provided: "
					+ value + ")!");
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String numFoldsTipText() {
		return "The number of folds to use for splitting the training set into smaller chunks for the base classifier.";
	}

	/**
	 * Set the verbose state.
	 * 
	 * @param value
	 *            the verbose state
	 */
	public void setVerbose(boolean value) {
		m_Verbose = value;
	}

	/**
	 * Gets the verbose state
	 * 
	 * @return the verbose state
	 */
	public boolean getVerbose() {
		return m_Verbose;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String verboseTipText() {
		return "Whether to ouput some additional information during building.";
	}

	/**
	 * Bagging method.
	 * 
	 * @param data
	 *            the training data to be used for generating the bagged
	 *            classifier.
	 * @throws Exception
	 *             if the classifier could not be built successfully
	 */
	public void buildClassifier(Instances data) throws Exception {
		Classifier[] base;
		int i;
		int n;
		int fromIndex;
		int toIndex;
		Instances train;
		double chunkSize;

		// can classifier handle the data?
		getCapabilities().testWithFail(data);

		// remove instances with missing class
		data = new Instances(data);
		data.deleteWithMissingClass();

		m_Vote = new Vote();
		base = new Classifier[getNumFolds()];
		chunkSize = (double) data.numInstances() / (double) getNumFolds();

		// stratify data
		if (getNumFolds() > 1) {
			data.randomize(data.getRandomNumberGenerator(getSeed()));
			data.stratify(getNumFolds());
		}

		// generate <folds> classifiers
		for (i = 0; i < getNumFolds(); i++) {
			base[i] = makeCopy(getClassifier());

			// generate training data
			if (getNumFolds() > 1) {
				// some progress information
				if (getVerbose())
					System.out.print(".");

				train = data.testCV(getNumFolds(), i);
			} else {
				train = data;
			}

			// train classifier
			base[i].buildClassifier(train);
		}

		// init vote
		m_Vote.setClassifiers(base);

		if (getVerbose())
			System.out.println();
	}

	/**
	 * Calculates the class membership probabilities for the given test
	 * instance.
	 * 
	 * @param instance
	 *            the instance to be classified
	 * @return preedicted class probability distribution
	 * @throws Exception
	 *             if distribution can't be computed successfully
	 */
	public double[] distributionForInstance(Instance instance) throws Exception {
		return m_Vote.distributionForInstance(instance);
	}

	/**
	 * Returns description of the classifier.
	 * 
	 * @return description of the classifier as a string
	 */
	public String toString() {
		if (m_Vote == null)
			return this.getClass().getName().replaceAll(".*\\.", "")
					+ ": No model built yet.";
		else
			return m_Vote.toString();
	}

	/**
	 * Returns the revision string.
	 * 
	 * @return the revision
	 */
	public String getRevision() {
		return RevisionUtils.extract("$Revision: 5306 $");
	}

	/**
	 * Main method for testing this class.
	 * 
	 * @param args
	 *            the options
	 */
	public static void main(String[] args) {
		runClassifier(new Dagging(), args);
	}
}
